The goal of this workshop is to build an end-to-end, streaming recommendations pipeline using the latest streaming analytics tools inside a portable (take-home) Docker Container in the cloud.

First, we create a data pipeline to interactively analyze, approximate, and visualize streaming data using modern tools such as Apache Spark, NiFi, Kafka, Zeppelin, iPython, and ElasticSearch.

Next, we extend our pipeline to use streaming data to generate personalized recommendation models from using popular machine learning, graph, and natural language processing techniques such as collaborative filtering, clustering, and topic modeling.

Lastly, we production-ize our pipeline and serve live recommendations to our users!

You'll Learn How To

• Create a complete, end-to-end streaming data analytics pipeline

• Interactively analyze, approximate, and visualize streaming data

• Generate machine learning, graph & NLP recommendation models

• Production-ize our ML models to serve recommendations in real-time

• Perform a hybrid on-premise and cloud deployment using Docker

• Customize this workshop environment to your specific use cases

Target Audience

• Data Scientists and Analysts interested in learning more about the streaming data pipelines that power their real-time machine learning models and visualizations